Machine learning methods for human brain imaging
Author: Fatos Tunay Yarman Vural
Publisher: Frontiers Media SA
Published: 2023-03-29
Total Pages: 160
ISBN-13: 2832519105
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Advanced Machine Learning Approaches for Brain Mapping
Author: Dajiang Zhu
Publisher: Frontiers Media SA
Published: 2024-04-10
Total Pages: 230
ISBN-13: 2832547575
DOWNLOAD EBOOKBrain mapping is dedicated to using brain imaging techniques such as MRI, CT, PET, EEG, and fNIRS to understand the brain anatomy, structure, and function, and how it contributes to cognition, behavior, and deficits of brain diseases. Recently, machine learning is in a stage of rapid development, and various new technologies are continuously introduced into the field, from traditional approaches
Machine Learning and Interpretation in Neuroimaging
Author: Georg Langs
Publisher: Springer
Published: 2012-11-11
Total Pages: 277
ISBN-13: 3642347134
DOWNLOAD EBOOKBrain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on Machine Learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning.
Neuroimaging in Epilepsy
Author: Harry Chugani, MD
Publisher: Oxford University Press
Published: 2010-11-23
Total Pages:
ISBN-13: 0199711526
DOWNLOAD EBOOKPerhaps the most important achievements in the field of epileptology in the past two decades have been in the neuroimaging and genetic breakthroughs as applied to patients with epilepsy. Indeed, neuroimaging has become a vital part in the study of epilepsy, affecting broad aspects of the disorder ranging from diagnosis and classification to treatment and prognosis. Neuroimaging in epilepsy encompasses many different approaches that have reached various levels of expertise across epilepsy centers worldwide. This book discusses every imaging modality used to gather information on epilepsy. Each technique is described by world experts and epilepsy centers worldwide.
Visual Analytics and Interactive Machine Learning for Human Brain Data
Author: Li Huang
Publisher:
Published: 2019
Total Pages: 152
ISBN-13:
DOWNLOAD EBOOKThis study mainly focuses on applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery. It mainly consists of two parts: multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate structural, functional and connectivity data to form a comprehensive visual context. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. For interactive machine learning, we propose a new visual analytics approach to interactive machine learning. In this approach, multi-dimensional data visualization techniques are employed to facilitate user interactions with the machine learning process. This allows dynamic user feedback in different forms, such as data selection, data labeling, and data correction, to enhance the efficiency of model building.
Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology
Author: Seyed Mostafa Kia
Publisher: Springer Nature
Published: 2020-12-30
Total Pages: 319
ISBN-13: 3030668436
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.
Machine Learning and Medical Imaging
Author: Guorong Wu
Publisher: Academic Press
Published: 2016-08-11
Total Pages: 514
ISBN-13: 0128041145
DOWNLOAD EBOOKMachine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques
Deep Learning Methods for Diffusion MRI in Early Development of the Human Brain
Author: Hamza Kebiri
Publisher:
Published: 2023
Total Pages: 0
ISBN-13:
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Machine Learning in Clinical Neuroimaging
Author: Ahmed Abdulkadir
Publisher: Springer Nature
Published: 2022-10-07
Total Pages: 190
ISBN-13: 3031178998
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2022, held in Conjunction with MICCAI 2022, Singapore in September 2022. The book includes 17 papers which were carefully reviewed and selected from 23 full-length submissions. The 5th international workshop on Machine Learning in Clinical Neuroimaging (MLCN2022) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). The papers are categorzied into topical sub-headings: Morphometry; Diagnostics, and Aging, and Neurodegeneration.
Machine Learning in Clinical Neuroimaging
Author: Ahmed Abdulkadir
Publisher: Springer Nature
Published: 2023-10-07
Total Pages: 183
ISBN-13: 3031448588
DOWNLOAD EBOOKThis book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track). The papers are categorzied into topical sub-headings on Machine Learning and Clinical Applications.
